如何准备训练数据进行图像分割 [英] How to prepare training data for image segmentation

问题描述

我正在使用边界框标记工具（例如BBox和YOLO标记）进行对象定位。我想知道是否有任何等效的标记工具可用于图像分割任务。学术界和研究界人士如何为这些图像分割任务准备数据集。最近的Kaggle竞赛

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I am using bounding box marking tools like BBox and YOLO marker for object localisation. I wanted to know is there any equivalent marking tools available for image segmentation tasks. How people in academia and research are preparing data sets for these image segmentation tasks. Recent Kaggle competition severstal-steel-defect-detection has pixel level segmentation information. Which tool they used to prepare this data?

解决方案

Generally speaking it is a pretty complex but a common task, so you'll likely be able to find several tools. Supervise.ly is a good example. Look through the demo to understand the actual complexity.

Another way is to use OpenCV to get some specific results. We did that, but results were pretty rough. Another problem is performance. There are couple reasons we use 4K video.

Long story short, we decided to implement a custom tool to get required results (and do that fast enough).

(see in action)

Just to summarize, if you want to build a training set for segmentation you have the following options:

1. Use available services (pretty much all of them will require additional manual work)
2. Use OpenCV to deal with a specially prepared input
3. Develop a custom solution to deal with a properly prepared input, providing full control and accurate results

The third option seems to be the most flexible solution. Here are some examples. Those are custom multi-color segmentation results. You might got an impression custom implementation is way more complex, but as it turned out if you properly implement some straight-forward algorithm you might be surprised with the result. We were interested in accurate pixel-perfect results:

(see in action)

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